{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "76511ab3-bda2-4e54-8188-7ba2ce18cd70",
   "metadata": {},
   "source": [
    "# 2.3.5决策树"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0259d4d1-b61e-4afc-a0c0-24e26d38abcc",
   "metadata": {},
   "source": [
    "## 2.决策控制树的复杂度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "05ca85b7-a2bf-4b0a-8e6c-4a5c6ab01554",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 1.000\n",
      "Accuracy on test set: 0.937\n"
     ]
    }
   ],
   "source": [
    "#导入模块\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "cancer = load_breast_cancer()\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    " cancer.data, cancer.target, stratify=cancer.target, random_state=42)\n",
    "tree = DecisionTreeClassifier(random_state=0)\n",
    "tree.fit(X_train, y_train)\n",
    "print(\"Accuracy on training set: {:.3f}\".format(tree.score(X_train, y_train)))\n",
    "print(\"Accuracy on test set: {:.3f}\".format(tree.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "01c46598-7598-4c09-bee2-93e25dd686e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.988\n",
      "Accuracy on test set: 0.951\n"
     ]
    }
   ],
   "source": [
    "tree = DecisionTreeClassifier(max_depth=4, random_state=0)                          #max_depth=4\n",
    "tree.fit(X_train, y_train)\n",
    "print(\"Accuracy on training set: {:.3f}\".format(tree.score(X_train, y_train)))\n",
    "print(\"Accuracy on test set: {:.3f}\".format(tree.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8677c20-e92d-4f12-926d-78921a194643",
   "metadata": {},
   "source": [
    "### 3.分析决策树"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5bbd664b-43d7-49df-a42f-272e7477b1fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import export_graphviz\n",
    "export_graphviz(tree, out_file=\"tree.dot\", class_names=[\"malignant\",\"benign\"],\n",
    " feature_names=cancer.feature_names, impurity=False, filled=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "010c70f7-2597-4ccc-bc1c-6c5088b8c494",
   "metadata": {},
   "outputs": [],
   "source": [
    "import graphviz\n",
    "with open(\"tree.dot\") as f:\n",
    " dot_graph = f.read()\n",
    "graphviz.Source(dot_graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b3cc5943-724b-4ea9-9e13-c67c21e6ec5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature importances:\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.01019737 0.04839825\n",
      " 0.         0.         0.0024156  0.         0.         0.\n",
      " 0.         0.         0.72682851 0.0458159  0.         0.\n",
      " 0.0141577  0.         0.018188   0.1221132  0.01188548 0.        ]\n"
     ]
    }
   ],
   "source": [
    "print(\"Feature importances:\\n{}\".format(tree.feature_importances_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "51146c49-2392-4246-a8f2-5b439a3c0914",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "# 假设 cancer 和 tree 已正确定义，这里需提前导入相关数据集和模型对象\n",
    "# 比如从 sklearn.datasets 导入乳腺癌数据集，构建决策树模型等\n",
    "def plot_feature_importances_cancer(model):\n",
    "    n_features = cancer.data.shape[1]\n",
    "    plt.barh(range(n_features), model.feature_importances_, align='center')      # 这两行有意思\n",
    "    plt.yticks(np.arange(n_features), cancer.feature_names)                      #\n",
    "    plt.xlabel(\"Feature importance\")\n",
    "    plt.ylabel(\"Feature\")\n",
    "\n",
    "plot_feature_importances_cancer(tree)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "b6c3a27f-b377-418d-a2f4-18da348520af",
   "metadata": {},
   "source": [
    "tree = mglearn.plots.plot_tree_not_monotone()\n",
    "display(tree)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "4ef35a83-f3cc-47f6-b4f1-3858ff4445c5",
   "metadata": {},
   "source": [
    "import pandas as pd\n",
    "ram_prices = pd.read_csv(\"data/ram_price.csv\")\n",
    "plt.semilogy(ram_prices.date, ram_prices.price)\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Price in $/Mbyte\")"
   ]
  },
  {
   "cell_type": "raw",
   "id": "be8c2759-c539-40e8-bbdc-19fa12422f14",
   "metadata": {},
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "# 利用历史数据预测2000年后的价格\n",
    "data_train = ram_prices[ram_prices.date < 2000]\n",
    "data_test = ram_prices[ram_prices.date >= 2000]\n",
    "# 基于日期来预测价格\n",
    "X_train = data_train.date[:, np.newaxis]\n",
    "# 我们利用对数变换得到数据和目标之间更简单的关系\n",
    "y_train = np.log(data_train.price)\n",
    "tree = DecisionTreeRegressor().fit(X_train, y_train)\n",
    "linear_reg = LinearRegression().fit(X_train, y_train)\n",
    "# 对所有数据进行预测\n",
    "X_all = ram_prices.date[:, np.newaxis]\n",
    "pred_tree = tree.predict(X_all)\n",
    "pred_lr = linear_reg.predict(X_all)\n",
    "# 对数变换逆运算\n",
    "price_tree = np.exp(pred_tree)\n",
    "price_lr = np.exp(pred_lr)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "3a67df94-1993-484f-a5c4-68ebcee87a51",
   "metadata": {},
   "source": [
    "plt.semilogy(data_train.date, data_train.price, label=\"Training data\")\n",
    "plt.semilogy(data_test.date, data_test.price, label=\"Test data\")\n",
    "plt.semilogy(ram_prices.date, price_tree, label=\"Tree prediction\")\n",
    "plt.semilogy(ram_prices.date, price_lr, label=\"Linear prediction\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5bcfa93-9daf-4fc5-a8b0-3a413993f252",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
